1. Field of the Invention
The present invention relates to detection of multifrequency (MF) communication signals such as dual-tone multifrequency (DTMF) signals.
2. Description of the Related Art
Multifrequency (MF) signaling allows control and data signals to be transmitted over conventional communication channels that may be used concurrently to carry other information, such as voice and data. A MF signal comprises two or more simultaneous (and relatively pure) tones that together represent a digit, character, or other information according to an agreed format. The communication channel can be provided by land lines (wire, cable, fiber optic, etc.), wireless signaling such as microwave and satellite links, and so forth.
Telephone networks use Dual-Tone Multi-Frequency (DTMF) signaling to transmit dialed digits between telephone sets and a central office or switch. Typically DTMF techniques are also used for voice-mail user interfaces, Interactive Voice Response (IVR) systems, and similar interactive control systems. DTMF signaling is a particular case of MF signaling that will be discussed herein in detail. However, the same issues of concern for DTMF signaling also arise in MF signaling generally.
A DTMF signal is a combination of two tones having well-defined frequencies typically selected from a standard pair of disjoint frequency band. For example, the current ITU-T standard (set forth in the ITU-T Q.24 recommendation) establishes a high band tone set of four tones with nominal frequencies at 1209, 1336, 1477, and 1633 hertz (Hz), respectively. The low band consists of four tones with nominal frequencies at 697, 770, 852, and 941 Hz. Of course, alternate MF standards could use different nominal frequencies and could use more or fewer tones in either or both bands.
A MF signal is detected using a MF detector. Such a product must be highly reliable to ensure proper detection of information represented in a valid MF signal while rejecting detection of non-MF signals that may also be received on the communication channel. A MF detector, such as a DTMF detector, must be able to determine (reliably) that a true MF signal (such as a dialed xe2x80x9c7xe2x80x9d) has been received. Determination of whether a received signal is an MF signal will be xe2x80x9cdigit detection.xe2x80x9d The detector must also be able to identify (reliably) the tone components representing the dialed digit, character, or other information.
High quality MF detectors must be designed for robustness in conditions of substantial signal impairments. Good signal detection performance means the MF detector must be capable of reliable digit detection in the presence of voice, non-MF data, random noise, and so forth (collectively xe2x80x9cnoisexe2x80x9d). This entails a requirement that the detector have both high sensitivity and high selectivity. xe2x80x9cSensitivityxe2x80x9d refers to the degree of assurance that the detector will correctly detect a DTMF signal when such a signal is received. xe2x80x9cSelectivityxe2x80x9d refers to the degree of assurance that the detector will reject a received signal when the signal is actually not a valid MF signal.
High selectivity is reflected in a low xe2x80x9ctalk-offxe2x80x9d rate, which is defined as a low rate at which non-MF signals are incorrectly identified as valid MF signals. Talk-offs typically arise from noise (such as voice signals, non-MF data signals, random noise, etc.). High sensitivity is reflected in a low xe2x80x9ctalk-downxe2x80x9d rate, i.e., a low rate at which valid MF signals are incorrectly classified as non-MF signals. Talk-downs may occur when true MF signals are transmitted, but one or more of their constituent tones are shifted in frequency from their nominal frequencies. A talk-down also can occur when a constituent tone in a valid MF is misshapen by the communication link and therefore contains frequency constituents other than the nominal frequency for that tone. A high quality detector must have both a low talk-off rate and a low talk-down rate.
Noise and distortion can also degrade the detector""s ability to identify correctly the digit, character, or other information represented by a valid MF signal. For example, a high quality DTMF detector must be able to identify correctly the two nominal tones of a DTMF signal. The tone identification must be highly reliable, even when the tones are subjected to frequency offsets and are superimposed with noise.
Most commercial DTMF detectors perform spectral computations, particularly based on modified Goertzel (MG) algorithms, to analyze incoming signals. A spectral decomposition of the signal is thereby generated, with a calculated energy for each of several nominal frequencies. Such detectors differ mainly in the way the results of the energy calculations are used for signal detection, i.e., to distinguish between true DTMF signals and other signals or noise.
A typical Goertzel-based detector will have a digital filter providing, for example, eight passbands centered at the eight nominal frequencies of the conventional DTMF standard. This basic structure constrains the computational and signal accumulation requirements of the detector within reasonable limits. On the other hand, such detectors are prone to false detections (talk-offs) when a non-DTMF signal has high and low band energy peaks close to center frequencies for the filter. Most MG filters therefore are unable to reject a signal (i.e., correctly detect the signal as a non-DTMF signal) if the signal has energy peaks within 3% of nominal DTMF frequencies.
To avoid this trade-off between accumulation time and frequency resolution, it has been proposed to estimate the frequencies of the incoming signal directly. See David L. Beard, DSP Implementation of a High Performance DTMF Decoder, ICSPAT-98 Proceedings 448-452 (1998). A frequency estimator was constructed by opening the feedback path of a Goertzel filter, thereby adapting the structure to measure a frequency, instead of measuring energy at a specified frequency.
This frequency estimation approach was able to satisfy some performance standards by including a running statistical analysis to determine a mean and standard deviation of the frequency estimates. Separate statistics thresholds were used for digit detection and in-digit stability. Further, effective performance involved selection of thresholds and parameters specific to a desired performance standard. With careful tailoring of the statistics thresholds, this detector had good talk-off performance in the presence of frequency offsets.
Detectors based on MG filters are also prone to false rejections (talk downs) when the signal xe2x80x9ctwistxe2x80x9d is too high. xe2x80x9cTwistxe2x80x9d is defined as the ratio of highest energy in the high band to the highest energy in the low band (expressed in dB). In a typical case, such a MF detector may be configured to reject signals having twist less than xe2x88x924 dB or greater than 4 dB. If the communication channel induces significant frequency-dependent attenuation, then the detector will have a high rate of false rejections.
U.S. Pat. No. 5,442,696, issued Aug. 15, 1995, to Lindberg et al., describes an alternative to Goertzel algorithm filters in a DTMF detector. An incoming signal is sliced into successive frames, and each frame is duplicated in several copies. For each frame, the copies of the frame are shaped with different shaping sequences (xe2x80x9ctapersxe2x80x9d) such as discrete prolate spheroidal sequences.
The detector then Fourier-transforms the shaped copies and, like the MG-based detectors, combines the results to form an estimate of the signal""s spectral distribution of energy. An inner product is then formed between the spectral estimate and each of several model energy distributions, and the inner product is compared with a threshold. Signal detection is then performed based on the inner products, if any, that pass the threshold test. Like MG filtering, therefore, this approach evaluates an incoming signal against discrete nominal frequencies and requires substantial computation and data accumulation for each frequency.
Canadian Patent No. 2,094,412 discloses a detector using an adjustable notch filter in each of the high and low bands. A frequency estimator identifies a frequency for a dominant spectral tone in each band. The frequency is then used to configure the corresponding notch filter, which in turn filters out the corresponding dominant component. This enables the system to measure the monotonicity of the received signal in each of the two bands by comparing the output of each notch filter to its corresponding input.
This monotonicity approach thus provides an alternative to spectral analysis and apparently avoids the heavy computational requirements of DFT-type systems while substantially reducing talk-off. Because it avoids generating an energy distribution, however, this system must rely on a relatively coarse measure of twist. Moreover, the effectiveness of the notch filters to detect monotonicity depends on frequency estimates with low variance. The Canadian reference addresses this constraint by requiring longer data accumulation blocks and by performing multiple measurements of each frequency.
The existing MF detectors of various types have therefore provided some improvement in suppressing false detections (talk-off), but have not addressed the parallel problem of false rejection. Further, the existing detectors also tend to rely on careful selection of computational procedures and parameters to produce good results. This prevents such detectors from being reliably optimized for the particular signal environment of a customer installation.
Hence, an unmet need has existed for a MF detector with both sensitivity (low rate of false rejections) and selectivity (low rate of false detections). Such a system should accept genuine MF signals, despite high twist values between different frequency bands. A further desirable feature would be rejection of signals with excessive frequency deviation, without applying inflexible upper bounds on deviation that could reduce sensitivity of the detector. Ideally, such a detector could be configured using customer-specific data in a reliable, repeatable procedure to provide performance optimized for the signal environment of a specific customer installation.
It is an object of the present invention to provide a multifrequency detector with improved talk-off performance in conjunction with improved talk-down performance.
The present invention provides a multifrequency detector and a corresponding method and corresponding computer software. The multifrequency detector comprises a frequency estimation module and a decision module. The frequency estimation module calculates a plurality of estimated frequencies for an input signal and quality information corresponding to the estimated frequencies. The input signal is based on a frame of a signal. The decision module performs digit detection based on the quality information and frame decision result for the frame.
In a second aspect, the invention provides a multifrequency detector comprising an energy analysis module and a decision unit, as well as a corresponding method and corresponding computer software. The energy analysis module generates a plurality of indices based on a plurality of spectral coefficients for a frame of a signal. The decision unit generates a decision level based on a combination of the indices and outputs the decision level for comparison with a predetermined threshold value.
In a third aspect, the invention provides a multifrequency detector comprising a decision logic and a neural network, as well as a corresponding method and corresponding computer software. The decision logic generates a plurality of indices from a plurality of spectral coefficients for an input signal representing a frame of a signal. The neural network comprises an input layer, a hidden layer, and an output layer. The input layer generates plural first signals based on the indices. The hidden layer generates plural second signals based on the first signals. The output layer generates a decision level based on the second signals, for comparison with a predetermined threshold value.
In a fourth aspect, the invention provides a method for training an adaptive multifrequency detector comprising a neural network. The method comprises two training operations. In a training operation the neural network is trained using back-propagation with input vectors from a heuristic signals database. In another training operation the neural network is trained using back-propagation with input vectors from an updated signals database generated from the heuristic signals database in accordance with a performance measurement for the neural network trained using the heuristic signals database.
Additional objects and advantages of the invention will be set forth in part in the following description and, in part, will be obvious therefrom or may be learned by practice of the invention.